Cmjv01i01p0071

J. Comp. & Math. Sci. Vol. 1(1), 71-74 (2009).

CHARACTER RECOGNITIONS VS HANDWRITING USING NEURAL NETWORK 1

Anil Rajput, 2Amit Dutta and 3Ramesh Prasad Aharwal

1

Head of the Department of Mathematics and Computer Science, Sadhu Vaswani College Bairagarh, Bhopal (M.P.), (India) Email: drar1234@yahoo.com 2 Programmer, Department of Computer Science Barkatullah University, Bhopal (M.P.), (India) Email: amitduttabub@yahoo.co.in 3 Asstt. Prof. Department of Mathematics and Computer Application, Govt. P. G. College Bareli (M.P.), (India) Email: ramesh_ahirwal_neetu@yahoo.com ABSTRACT The aim of this research paper is to analyze character recognitions vs handwriting using Neural Network. We have used MATLAB software for our experiment.

Key words: Neural Network, Back Propagation, Firing rules, MATLAB.

INTRODUCTION Back-propagation neural network with one hidden layer was used to create an adaptive character recognition system. The system was trained and evaluated with Printed text, as well as several different forms of handwriting provided by both male and female participants3. Experiments tested.

(1) The effect of set size on recognition accuracy with printed text, and (2) The effect of handwriting style on recognition accuracy.

Results showed reduced accuracy in recognizing printed text when differentiating between more than 12 characters. The handwriting style of the subjects had varying and drastic effects on recognition accuracy which illuminated some of the problems with the systems character encoding. There exist several different techniques for recognizing characters.

One distinguishes characters by the number of loops in a character and the direction of their concavities. Another common technique uses back-propagation in a neural network and we have investigated how good a neural network solves the character recognition problem. The Character Recognition System must first be

[ 72 ] created through a few simple steps in order to prepare it for presentation into MATLAB. The matrices of each letter of the alphabet must be created along with the network structure. In addition, one must understand how to pull the Binary Input Code from the matrix, and how to interpret the Binary Output Code, which the computer ultimately produces1.

EXPERIMENTS  The Effect of Handwriting Style on Character Recognition To test the effect handwriting style has Male 1:

Male 2:

Female 1:

Female 2:

on character recognition with this system, samples from the four subjects were scanned and converted to a series of Matlab vectors. To test in an environment where 100% accuracy was obtainable, only the first 8 letters of each sample were used. This also reduced the amount of time and processing power needed to run the experiment. Each character image was converted to a Matlab vector three times, each time in a slightly different position. Letters from the sentence in the handwriting sample were used to create the test set to determine accuracy.

The neural network was not exposed to the test set during training.

[ 73 ]  Firing rules The firing rule is an important concept in neural networks and accounts for their high flexibility. A firing rule determines how one calculates whether a neuron should fire for any input pattern. It relates to all the input patterns, not only the ones on which the node was trained5.

A simple firing rule can be implemented by using Hamming distance technique4. The rule goes as follows: Take a collection of training patterns for a node, some of which cause it to fire (the 1-taught set of patterns) and others which prevent it from doing so (the 0-taught set). Then the patterns not in the collection cause the node to fire if, on comparison, they have more input elements in common with the 'nearest' pattern in the 1-taught set than with the 'nearest' pattern in the 0-taught set. If there is a tie, then the pattern remains in the undefined state.

For example, a 3-input neuron is taught to output 1 when the input (X1, X2 and X3) is 111 or 101 and to output 0 when the input is 000 or 001. Then, before applying the firing rule, the truth table is;

Therefore, the 'nearest' pattern is 000 which belongs in the 0-taught set. Thus the firing rule requires that the neuron should not fire when the input is 001. On the other hand, 011 is equally distant from two taught patterns that have different outputs and thus the output stays undefined (0/1).

By applying the firing in every column the following truth table is obtained; X1: 0

0

0

0

1

1

1

1

X2: 0

0

1

1

0

0

1

1

X3: 0

1

0

1

0

1

0

1

OUT: 0

0

0

1

1

1

0/1 0/1

The difference between the two truth tables is called the generalisation of the neuron.

Therefore the firing rule gives the neuron a sense of similarity and enables it to respond 'sensibly' to patterns not seen during training.

RESULT  Handwriting Style vs. Recognition Accuracy

X1: 0

0

0

0

1

1

1

1

X2: 0

0

1

1

0

0

1

1

X3: 0

1

0

1

0

1

0

1

OUT: 0

0 0/1 0/1 0/1

1

Table 1 Handwriting Style vs. Recognition Accuracy Experiment

Epochs MSE

Accuracy

Female 1

1225

0.0091

62.5

Female 2

417

2 0.0099

50

0.0139

37.5

0.0097

50

0/1 1

As an example of the way the firing rule is applied, take the pattern 010. It differs from 000 in 1 element, from 001 in 2 elements, from 101 in 3 elements and from 111 in 2 elements.

Male 1 Male 2

10000 (max) 3687

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3. Hamza A. Ali, Back propagation neural network Arabic characters classification

module utilizing Microsoft Word, Journal of Computer Science, 4 (9), 744-751 (2008).

4. Reza G. A., Hand written Farsi character recognition using artificial neural network, International Journal of Computer Science and Information Security, 4, 1-2 (2009).

5. Smith S.J. and Baurgoin M.O., "Handwritten character classification using nearest neighbor in large database," IEEE Trans. on Pattem and Machine Intelligence, 16(10), 915-919 (1994).